Link Weight Prediction with Node Embeddings

Yuchen Hou, Lawrence B. Holder

Abstract:

Application of deep learning has been successful in various domains such as im-
age recognition, speech recognition and natural language processing. However,
the research on its application in graph mining is still in an early stage. Here we
present the first generic deep learning approach to the graph link weight prediction
problem based on node embeddings. We evaluate this approach with three differ-
ent node embedding techniques experimentally and compare its performance with
two state-of-the-art non deep learning baseline approaches. Our experiment re-
sults suggest that this deep learning approach outperforms the baselines by up to
70% depending on the dataset and embedding technique applied. This approach
shows that deep learning can be successfully applied to link weight prediction to
improve prediction accuracy.